Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications by Pradeep Rawat, Robin Singh Bhadoria, Punit Gupta, Priti Dimri, G. P. Saroha

“…The proposed model is inspired from Big-Bang Big-Crunch algorithm in astrology. The work has been compared with a genetic algorithm, Particle swarm optimization and TOPSIS algorithm. …”
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A novel pulse-current waveform circuit for low-energy consumption and low-noise transcranial magnetic stimulation by Xinhua Tan, Ao Guo, Jiasheng Tian, Yingwei Li, Yingwei Li, Jian Shi

“…This study proposes a novel, non-resonant, high-frequency switching design controlled by high-frequency pulse-width modulation (PWM) voltage excitation to achieve ideal pulse-current waveforms that minimize both clicking noise and heat generation from the TMS coil.MethodFirst, a particle swarm optimization algorithm was used to optimize the pulse-current waveform, minimizing both the resistance loss and clicking noise (vibration energy) generated by the TMS coils. …”
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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…The two sets of selected features are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approach respectively.  …”
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Machine learning-based multiphysics model for corrosion fatigue crack propagation in aluminum alloy by Hongkun Wang, Zhenshuang Wu, Dongmei Jiang, Haitao Wang, En-Hou Han

“…In view of the complexity and nonlinearity of model parameter optimization, a particle swarm optimization algorithm was utilized to mutually feedback experimental measurement results with simulation results, optimizing the unknown parameters in the model to obtain a high-fidelity corrosion fatigue model. …”
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Green-fitting scheduling equilibrium model of virtual power plant based on cooperative game with improved shapley value under new-type power system by Shuo Zhang, Luming Pang, Yingzi Li, Yuanli Chen, Kangxiang Li, Meixia Zheng

“…To solve the model effectively, the improved particle swarm optimization algorithm has been employed. …”
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Multi-Modal Federated Learning Over Cell-Free Massive MIMO Systems for Activity Recognition by Seyed Mohammad Sheikholeslami, Pai Chet Ng, Jamshid Abouei, Konstantinos N. Plataniotis

“…Additionally, we employ a modified Particle Swarm Optimization (PSO) algorithm for efficient resource allocation. …”
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State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model by Jun Chen, Yan Liu, Jun Yong, Cheng Yang, Liqin Yan, Yanping Zheng

“…This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learning Machine (ELM) to improve prediction accuracy. …”
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Collaborative Planning for Distribution Network and PV-ESS in the Marketization Environment by Ping RAO, Yue XIANG, Haotian YAO, Youbo LIU, Junyong LIU

“…The double-layer improved particle swarm optimization algorithm was applied to run the model. …”
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Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury by Yukun Wang, Xuebo Chen

“…Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. …”
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PID Control and Input Shaping for Quadrotor UAV Stabilization and Payload Swing Reduction by Liyana Ramli, Izzuddin M. Lazim, Amalin Aisya Awi, Aina Syazwin Shukor

“…To enhance swing suppression, the input shaper parameters are optimized using the Particle Swarm Optimization (PSO) algorithm. …”
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Analyzing adjustment and verification errors in electric metering devices for smart power systems considering multiple environmental factors by Chuanliang He, Xin Xia, Bo Zhang, Wei Kang, Jinxia Zhang, Haipeng Chen

“…Then, an error adjustment model based on gated recurrent unit-attention is constructed, and the particle swarm optimization algorithm is adopted for the purpose of optimizing hyperparameters. …”
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Rolling Bearing Fault Diagnosis Based on VMD-DWT and HADS-CNN-BiLSTM Hybrid Model by Luchuan Shao, Bing Zhao, Xutao Kang

“…The vibration signals are first reconstructed using a genetic algorithm (GA)-optimized VMD and particle swarm optimization (PSO)-optimized DWT for noise suppression. …”
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Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine by FU Qiang, HU Dong, YANG Tongliang, LUO Guoqing, TAN Weimin

“…Subsequently, the compressed data was used as the input signal for the D-MKELM. Particle swarm optimization(PSO) algorithm was applied to optimize critical parameters, enabling intelligent fault diagnosis.ResultsResults demonstrate that the proposed method, using only a small amount of bearing diagnostic data, automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM. …”
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Energy-Efficient Deployment Simulator of UAV-Mounted Base Stations Under Dynamic Weather Conditions by Gyeonghyeon Min, Jaewoo So

“…In this paper, we propose an energy-efficient UAV-MBS deployment scheme in multi-UAV-MBS networks using a hybrid improved simulated annealing–particle swarm optimization (ISA-PSO) algorithm to find the 3D position and transmission power of each UAV-MBS. …”
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Regenerative Braking in PV-Mounted Electric Vehicle With Reduced Switch VSI-Driven BLDC Motor and HAP-FUP Controller by Ron Carter S. B., Thangavel S.

“…Maximum energy is extracted from the PV module via employment of a particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm, while propulsion is provided through utilization of a brushless direct current (BLDC) motor driven by reduced switch voltage source inverter (VSI). …”
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Bayesian Uncertainty Quantification of Reflooding Model With PSO–Kriging and PCA Approach by Ziyue Zhang, Dong Li, Nianfeng Wang, Meng Lei

“…As reflooding is a vital stage to cool the core and prevent serious accidents and uncertainties exist in the important results of the program because of the complexity of the phenomena, IUQ is performed for reflooding models in this study based on Bayesian theory and Markov chain Monte Carlo (MCMC) algorithm. In order to solve the problem of large time costs in sampling and inefficient use of transient sample points, particle swarm optimization (PSO)–Kriging model and principal component analysis (PCA) are adopted in this paper. …”
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Application of data twinning based on deep time series model in smart city traffic flow prediction by Li Gao

“…Abstract This paper introduces an intelligent traffic flow prediction system that combines data twinning and deep learning, aiming to improve the prediction accuracy and model adaptability by integrating grey prediction model (GM(1,1)), long-short-term memory network (LSTM) and particle swarm optimization (PSO). The system construction starts from physical layer data acquisition, deals with missing data through smoothing and interpolation, and applies the GM(1,1) model to construct a digital twin layer for preliminary prediction. …”
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Virtualized Network Function Consolidation Based on Multiple Status Characteristics by Dandan Qi, Subin Shen, Guanghui Wang

“…The neural network is trained using the particle swarm optimization algorithm (PSO). VCMM migrates VNFs on servers to be turned off by adopting a greedy mechanism. …”
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A clustering-based co-allocation of battery swapping stations and wind-photovoltaic plants in radial distribution systems by Hamed Shams, Naghi Rostami, Behnam Mohammadi Ivatloo

“…A K-means clustering method is implemented to classify price, energy demand, wind, and photovoltaic generation into appropriate clusters embedded into the particle swarm optimization (PSO) algorithm. The decision variables of PSO are the wind-photovoltaic system capacity and hybrid system placement to supply the EV load demand for battery swapping stations. …”
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Prediction model for oil seal performance parameters based on PSO-MLP-KAN by Weixing Yan, Mingshuo Shi, Pengbo Xiao, Kui Zhang, Xin Wu

“…To accurately, efficiently, and stably predict the sealing performance of oil seals, this study proposes a prediction method based on a combination of the Kolmogorov–Arnold network (KAN) and multi-layer perceptron (MLP) optimized by particle swarm optimization (PSO) algorithm. …”
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